Off-line Signature Verification System Research Articles

One-Class Arabic Signature Verification: A Progressive Fusion of Optimal Features

Signature verification is regarded as the most beneficial behavioral characteristic-based biometric feature in security and fraud protection. It is also a popular biometric authentication technology in forensic and commercial transactions due to its various advantages, including noninvasiveness, user-friendliness, and social and legal acceptability. According to the literature, extensive research has been conducted on signature verification systems in a variety of languages, including English, Hindi, Bangla, and Chinese. However, the Arabic Offline Signature Verification (OSV) system is still a challenging issue that has not been investigated as much by researchers due to the Arabic script being distinguished by changing letter shapes, diacritics, ligatures, and overlapping, making verification more difficult. Recently, signature verification systems have shown promising results for recognizing signatures that are genuine or forgeries; however, performance on skilled forgery detection is still unsatisfactory. Most existing methods require many learning samples to improve verification accuracy, which is a major drawback because the number of available signature samples is often limited in the practical application of signature verification systems. This study addresses these issues by presenting an OSV system based on multifeature fusion and discriminant feature selection using a genetic algorithm (GA). In contrast to existing methods, which use multiclass learning approaches, this study uses a one-class learning strategy to address imbalanced signature data in the practical application of a signature verification system. The proposed approach is tested on three signature databases (SID-Arabic handwriting signatures, CEDAR (Center of Excellence for Document Analysis and Recognition), and UTSIG (University of Tehran Persian Signature), and experimental results show that the proposed system outperforms existing systems in terms of reducing the False Acceptance Rate (FAR), False Rejection Rate (FRR), and Equal Error Rate (ERR). The proposed system achieved 5% improvement.

Signature Verification Using Neural Network

Abstract: In this paper we are about to present a latest improved off-line signature verification system using global and texture features of the signatures. This version is based on the technique that applies pre-processing on the signature to get a binary image and then calculate the global and features points from it and than maintain a updated vector. All calculations are done on the basis of these feature points. The feature vector obtained from the global and texture features is used to compare with the feature vector of incoming testing signature. Based on the values obtained, the network will decide the appropriateness of the signature. The suggested scheme discriminates between original and the forged signatures using artificial neural network (ANN) for training and verification of signatures. The method takes care of simple and the random forgeries and the skilled forgeries are also eliminated in greater extent. The objective of the work is to reduce two vital parameters, False Acceptance Rate (FAR) and the False Rejection Rate (FRR). So the results are expressed in terms of FAR and FRR and subsequently comparative analysis has been made with standard existing techniques. Results obtained by our proposed algorithm are more efficient than most of the existing techniques.

Off-line signature verification system using features linear mapping in the candidate points

Off-line signature verification system using features linear mapping in the candidate points

Offline signature verification system: a graph neural network based approach

Offline signature verification system: a graph neural network based approach

Offline Signature Verification System Using SVM Classifier with Image Pre-processing Steps and SURF Algorithm

A signature is a mark or name that represents the identity of the people and the Signature Verification System (SVS) is used to validate the identity of people. The signature verification system is mostly used for bank cheques, vouchers, intelligence agencies and others. There are two types of SVS which are online and offline signature verification systems. The paper deals with an offline signature verification system. The proposed system consists of four main stages, (i) image acquisition, (ii) image pre-processing, (iii) feature extraction and (iv) classification. The image pre-processing steps involved binarization, noise removal using Gaussian filter and image resizing and thinning. In the feature extraction stage, Bag-of-Features with the Speeded Up Robust Features (SURF) extractor was utilized. In the third stage, the Support Vector Machine (SVM) classifier is used. Lastly, the confusion matrix and the verification rate were used to evaluate the performance of the classifier. In this paper, we implement and compare the performance of the signature verification system without entering the user ID and the signature verification system entering the user ID. For the ratio of 75% and 25% of the training and testing, respectively, the average accuracy for the signature verification system without entering the user ID is 71.36%, whereas the average accuracy for the signature verification system entering the user ID is 79.55%.

From text to signatures: Knowledge transfer for efficient deep feature learning in offline signature verification

Handwritten signature is a common biometric trait, widely used for confirming the presence or the consent of a person. Offline Signature Verification (OSV) is the task of verifying the signer using static signature images captured after the finish of signing process, with many applications especially in the domain of forensics. Deep Convolutional Neural Networks (CNNs) can generate efficient feature representations, but their training is data-intensive. Since limited training data is an intrinsic problem of an OSV system’s development, this work focuses on addressing the problem of learning informative features by employing prior knowledge from a similar task in a domain with an abundance of training data. In particular, we demonstrate that an appropriate pre-training of a CNN model in the task of handwritten text-based writer identification task, can dramatically improve the efficiency of the CNN in the OSV task, enabling to obtain state-of-the-art performance with an order of magnitude less training signature samples. In the proposed scheme, after the pre-training of the CNN in writer identification task through specially processed handwritten text data, the learned features are tailored to the signature problem though a metric learning stage that utilizes contrastive loss to learn a mapping of the signatures’ features to a latent space that suits the OSV task. At the final stage, the proposed scheme utilizes Writer-Dependent (WD) classifiers learned on a few reference samples from each writer. Our system is tested on the three challenging signature datasets, CEDAR, MCYT-75 and GPDS300GRAY. The obtained accuracy in terms of Equal Error Rates (EER) is statistically equivalent to the popular SigNet CNN, despite a significantly smaller training set of signature images and no use of skilled forgeries signatures during training.

Adoption of Off-Line Signature Verification and Forgery Detection System Using Additive Fuzzy and TS Modelling Technique in Financial Auditing and Forensics Investigation

This paper presents a robust signature verification and forgery detection system using Additive fuzzy and TS modeling technique. The features of various handwritten signatures are sampled with proper analysis and encapsulated to devise an effective verification system. Grid method was used to extract features angles for detection of forgeries and verification of genuine signatures. In financial Accounting, Auditing and Forensic Investigation, signature forgery could occur in various ways. This could be carried out on papers, sales documents such as invoices or inventory procurement requisition paper, title documents on landed property or other tangible assets. It is also perpetrated on payment authorization such as cheques, payment vouchers both in cash and on bills. During this exercise, the fraud perpetrators perfect their concentration on the surface paper, and trace the original signature from the mandate given earlier. It has been difficult to use accounting and auditing professions to track down financial fraud in Nigeria mostly with the problem of unearthing ingenious fraud. Exponential membership function was used to fuzzified the derived functions, and modified into structural parameters suitable to adapt to any possible variations that may result from handwriting styles and also to reflect any other factors due to scripting of a signature. The proposed system is tested on a large database of signatures obtained from 40 subjects.

Multi-Layer Perceptron Neural Network for an Offline Signature Verification System

Multi-Layer Perceptron Neural Network for an Offline Signature Verification System

Machine learning-based offline signature verification systems: A systematic review

The offline signatures are the most widely adopted biometric authentication techniques in banking systems, administrative and financial applications due to its simplicity and uniqueness. Several automated techniques have been developed to anticipate the genuineness of the offline signature. However, the recapitulate of the existing literature on machine learning-based offline signature verification (OfSV) systems are available in a few review studies only. The objective of this systematic review is to present the state-of-the-art machine learning-based models for OfSV systems using five aspects like datasets, preprocessing techniques, feature extraction methods, machine learning-based verification models and performance evaluation metrics. Thus, five research questions were identified and analysed in this context. This review covers the articles published between January 2014 and October 2019. A systematic approach has been adopted to select the 56 articles. This systematic review revealed that recently, the deep learning-based neural network attained the most promising results for the OfSV systems on public datasets. This review consolidates the state-of-the-art OfSV systems performances in selected studies on five public datasets (CEDAR, GPDS, MCYT-75, UTSig and BHSig260). Finally, fifteen open research issues were identified for future development.

An Ensemble Based Offline Handwritten Signature Verification System

In the field of security and forgery prevention, handwritten signatures are the most widely recognized biometric since long and also most practical. Although handwritten signature verification systems are studied using both On-line and Off-line approaches, Off-line signature verification systems are more difficult to compare to On-line verification systems. This is due to the lack of dynamic information, viz. a database which constantly stores the latest signature of the person. In the paper an approach using ensemble methods are adopted to classify a signature as forgery or not. In proposed system, three classifiers, viz, one unsupervised, viz. Fuzzy C-Means (FCM) and two supervised classifiers, viz. Naive Bayes (NB) and Support Vector Machine (SVM) are used as base classifiers. An attempt is made to compare the different approaches. We attempt both the categories of classification not only because both of them are applicable in this particular case but also with an objective of finding out which performs better. In most cases it is observed that Naive Bayes and Ensemble are comparable as they exhibit better performance than the other two. But among them, in most of the cases Ensemble classifier performs better than the Naive Bayes and consequently we have taken the Ensemble as a final classifier.

A Robust Approach to Authentication of Handwritten Signature Using Voting Classifier

This research paper’s main motive is to improve the recognition rate of Offline Signature Verification system. In our research, decisions of three classifiers i.e., Multilayer Perceptron, Random Forest Classifier and Naive Bayes are combined using voting classifier to determine the output. The softwares used for this research are WEKA and Matlab. Performance of this approach is tested on CEDAR dataset for writer dependent model. Overall recognition rate of whole dataset of 55 users is 91.25%. Out of the dataset, the recognition rate of 45 users is above 85%.

Offline signature verification system: a novel technique of fusion of GLCM and geometric features using SVM

In the area of digital biometric systems, the handwritten signature plays a key role in the authentication of a person based on their original samples. In offline signature verification (OSV), several problems exist that are challenging for verification of authentic or forgery signature by the digital system. Correct signature verification improves the security of people, systems, and services. It is applied to uniquely identify an individual based on the motion of pen as up and down, signature speed, and shape of a loop. In this work, the multi-level features fusion and optimal features selection based automatic technique is proposed for OSV. For this purpose, twenty-two Gray Level Co-occurrences Matrix (GLCM) and eight geometric features are calculated from pre-processing signature samples. These features are fused by a new parallel approach which is based on a high-priority index feature (HPFI). A skewness-kurtosis based features selection approach is also proposed name skewness-kurtosis controlled PCA (SKcPCA) and selects the optimal features for final classification into forged and genuine signatures. MCYT, GPDS synthetic, and CEDAR datasets are utilized for validation of the proposed system and show enhancement in terms of Far and FRR as compared to existing methods.

Multi-representational learning for Offline Signature Verification using Multi-Loss Snapshot Ensemble of CNNs

Offline Signature Verification (OSV) is a challenging pattern recognition task, especially in the presence of skilled forgeries that are not available during training. This study aims to tackle its challenges and meet the substantial need for generalization for OSV by examining different loss functions for Convolutional Neural Network (CNN). We adopt our new approach to OSV by asking two questions: 1. which classification loss provides more generalization for feature learning in OSV?, and 2. How integration of different losses into a unified multi-loss function lead to an improved learning framework?These questions are studied based on analysis of three loss functions, including cross entropy, Cauchy-Schwarz divergence, and hinge loss. According to complementary features of these losses, we combine them into a dynamic multi-loss function and propose a novel ensemble framework for simultaneous use of them in CNN. Our proposed Multi-Loss Snapshot Ensemble (MLSE) consists of several sequential trials. In each trial, a dominant loss function is selected from the multi-loss set, and the remaining losses act as a regularizer. Different trials learn diverse representations for each input based on signature identification task. This multi-representation set is then employed for the verification task. An ensemble of SVMs is trained on these representations, and their decisions are finally combined according to the selection of most generalizable SVM for each user.We conducted two sets of experiments based on two different protocols of OSV, i.e., writer-dependent and writer-independent on three signature datasets: GPDS-Synthetic, MCYT, and UT-SIG. Based on the writer-dependent OSV protocol, On UT-SIG, we achieved 6.17% Equal Error Rate (EER) which showed substantial improvement over the best EER in the literature, 9.61%. Our method surpassed state-of-the-arts by 2.5% on GPDS-Synthetic, achieving 6.13%. Our result on MCYT was also comparable to the best previous results. The second set of experiments examined the robustness of our proposed method to the arrival of new users enrolled in the OSV system based on the writer-independent protocol. The results also confirmed that our proposed system efficiently performed the verification of new users enrolled in the OSV system.

A Clustering-based Offline Signature Verification System for Managing Lecture Attendance

A Clustering-based Offline Signature Verification System for Managing Lecture Attendance

Long range correlation of preceded pixels relations and application to off‐line signature verification

Lately, off-line signature verification systems have been reintroduced based on the idea of modelling the signature images with various relations among their pixels. In this paper, a modified version of the partially ordered set feature extraction procedure is presented by enabling distant range interactions between preceded relations of pixel groups. In this way, the spatial diversity of correlation that exists among signature features can be exploited. The motive behind this approach is related to our belief that the particular idiosyncratic writing style characteristics of each individual will be present over the whole length of the signature. Experiments involve the well-known Center of Excellence for Document Analysis and Recognition (CEDAR) and Ministerio de Ciencia y Tecnologia (MCYT) datasets in two popular writer dependent training modes: In the first mode, genuine and simulated forgeries were utilized, while in the second one genuine and random forgeries only were utilized during the training stage of the classifier. In both cases, the testing phase is exploited with genuine, simulated and random forgeries while receiver operating characteristic along with decision oriented FAR, FRR and average error metrics are assessing the proposed feature extraction method. The results obtained, show that the long range correlation of grid features can be efficiently employed for off-line signature verification.

基于方向特征的维吾尔文离线签名鉴别

本文以弥补我国少数民族离线签名鉴别体系中这一漏洞的前提下，结合维吾尔文手写签名的书写风格及特点，提出了一种基于16维方向的维吾尔文离线签名鉴别方法。实验中，对10个维吾尔族人的600个手写签名样本进行签名预处理和特征提取之后，分别用三种距离分类器来进行了签名鉴别，得到的最高签名总正确率为92.58%。实验结果表明，本文提出的方法对于离线的维吾尔文手写签名鉴别来说是一种较有效的鉴别方法。 In this paper, in order to compensate the blank of ethnic minorities off-line signature verification system in our country, a Uyghur off-line signature verification method based on 16 dimensional directional features was proposed by combining with the writing style and nature of Uyghur handwritten signature. In the experiment, three kinds of distance classifier were used separately to signature verification after preprocessing and feature extracting 600 handwritten signature samples from the 10 Uyghur people, and 92.58% of the highest total correct verification rate was obtained. Experimental results show that, the method proposed in this paper is a more effective verification method for off-line handwritten Uyghur signature.

Comparative Analysis of Offline Signature Verification System

Comparative Analysis of Offline Signature Verification System

Comparative Analysis of Offline Signature Verification System

Comparative Analysis of Offline Signature Verification System

Offline Signature Verification and Forgery Detection Based on Computer Vision and Fuzzy Logic

<p>Automated signature verification and forgery detection has many applications in the field of Bank-cheque processing,document authentication, ATM access etc. Handwritten signatures have proved to be important in authenticating a person's identity, who is signing the document. In this paper a Fuzzy Logic and Artificial Neural Network Based Off-line Signature Verification and Forgery Detection System is presented. As there are unique and important variations in the feature elements of each signature, so in order to match a particular signature with the database, the structural parameters of the signatures along with the local variations in the signature characteristics are used. These characteristics have been used to train the artificial neural network. The system uses the features extracted from the signatures such as centroid, height – width ratio, total area, I<sup>st</sup> and II<sup>nd</sup> order derivatives, quadrant areas etc. After the verification of the signature the angle features are used in fuzzy logic based system for forgery detection.</p>

Off‐line signature verification: upper and lower envelope shape analysis using chord moments

Signature is an important and useful behavioural biometric which exhibits significant amount of non-linear variability. In this study, the authors concentrate on finding an envelope shape feature known as ‘chord moments’. Central moments such as the variance, skewness and kurtosis along with the first moment (mean) are computed from sets of chord lengths and angles for each envelope reference point. The proposed chord moments adequately quantify the spatial inter-relationship among upper and lower envelope points. The moment-based approach significantly reduces the dimension of highly detailed chord sets and is experimentally found to be robust in handling non-linear variability from signature images. The proposed chord moments coupled with the support vector machine classifier lead to a writer dependent off-line signature verification system that achieves state-of-the-art performance on the noisy Center of Excellence for Document Analysis and Recognition database.